Decorate (3.7)

DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples

Comprehensive experiments have demonstrated that this technique is consistently more accurate than the base classifier, Bagging and Random Forests.Decorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets.

For more details see:


Melville, R.J.

Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples.In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003.


Melville, R.J.

Mooney (2004).Creating Diversity in Ensembles Using Artificial Data.

Information Fusion: Special Issue on Diversity in Multiclassifier Systems..

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.


Decorate Options

E: Desired size of ensemble. (default 10)

R: Factor that determines number of artificial examples to generate. Specified proportional to training set size. (default 1.0)

S: Random number seed. (default 1)

I: Number of iterations. (default 10)

W: Full name of base classifier. (default: weka.classifiers.trees.J48)

U: Use unpruned tree.

O: Do not collapse tree.

C: Set confidence threshold for pruning. (default 0.25)

M: Set minimum number of instances per leaf. (default 2)

R: Use reduced error pruning.

N: Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)

B: Use binary splits only.

S: Don't perform subtree raising.

L: Do not clean up after the tree has been built.

A: Laplace smoothing for predicted probabilities.

J: Do not use MDL correction for info gain on numeric attributes.

Q: Seed for random data shuffling (default 1).

Select target column
Choose the column that contains the target variable.
Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Missing values, Nominal class, Binary class, Missing class values] Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Missing class values, Only multi-Instance data] min # Instance: 15

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

Training data

Output Ports

Trained model

Popular Predecessors

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Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.


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